The invention relates generally to cloud-based systems to facilitate enterprise analytics. In particular, embodiments may facilitate automated feature deployment for active analytics microservices in a cloud-based architecture.
An enterprise may collect operating data from a set of enterprise system devices. For example, the enterprise may deploy sensors associated with one or more industrial assets (e.g., wind farm devices, turbine engines, etc.) and collect data as those assets operate. Note that the amount of industrial data that can be collected in this way may be significant in terms of volume, velocity, and/or variety. To help extract insight from the data, the enterprise may employ a “cloud-based” industrial internet platform to facilitate creation of applications to turn real-time operational data into insights. As used herein, a “cloud-based” industrial platform may help connect machines to collect key industrial data and stream the information to the cloud and/or leverage services and development tools to help the enterprise focus on solving problems. In this way, the cloud-based industrial platform may help an enterprise deploy scalable services and end-to-end applications in a secure environment.
In some cases, data scientists may use the cloud-based industrial platform to create analytic code algorithms (in Java, python, etc.). These algorithms may be wrapped in code to create analytics microservices that can be deployed to, and executed in, the cloud-based architecture. When actively deployed, an analytics microservice may respond to requests from users (and might be working on queue of such requests). Occasionally, features may be modified or added to a microservice. For example, a microservice might be modified such that synchronous requests (which wait for a response) are handled similar to asynchronous requests (to avoid timeouts). Other features might be associated with security elements, etc. When a feature is modified or added, it may need to be added to currently deployed analytics microservices. Note that manually updating potentially thousands of active microservices, each of which may be currently processing requests, might be impractical. Also note that service outages should be avoided if at all possible. Thus, it may be desirable to provide systems and methods to automatically facilitate microservice feature deployment in an efficient and accurate manner.